Appl. Spatial Analysis DOI 10.1007/s12061-009-9044-6
Explaining Spatial Homogamy. Compositional, Spatial and Regional Cultural Determinants of Regional Patterns of Spatial Homogamy in the Netherlands Karen Haandrikman & Leo J. G. van Wissen & Carel N. Harmsen
Received: 24 October 2008 / Accepted: 3 December 2009 # The Author(s) 2010. This article is published with open access at Springerlink.com
Abstract Spatial homogamy, or sharing a similarity in geographical origin, is an under-researched dimension in homogamy studies. In the Netherlands, people tend to choose spatially homogamous partners. Moreover, there is considerable regional variation in spatial homogamy, even when residential location and population density are controlled for. This study aims to explain the regional variation in spatial homogamy by means of a spatial regression. Three sets of explanations are taken into account: compositional effects, spatial determinants, and regional cultural differences. The data used consists of a unique geo-coded micro dataset on all new cohabiters in the Netherlands in 2004 (N=289,248), combined with other data from varying sources. In the spatial regression, the dependent variable is the standardized distance coefficient, based on the distance between partners before cohabitation, standardised for the average distance to other inhabitants. We find that especially educational, income and cultural differences contribute to the regional variation in spatial homogamy. Keywords Partner choice . Homogamy . Spatial patterns . Standardized distance coefficient . Spatial econometrics . The Netherlands
Introduction Studies on assortative mating have found that around the world, individuals tend to look for a partner with similar characteristics. Homogamy, or the similarity between K. Haandrikman (*) : L. J. G. van Wissen Population Research Centre, Faculty of Spatial Sciences, University of Groningen, Groningen, the Netherlands e-mail:
[email protected] C. N. Harmsen Statistics Netherlands, The Hague, the Netherlands
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married or cohabitating partners, has mostly been studied from a sociological perspective; similarity in these studies is defined in terms of social class, education, religion, or ethnic background. Implicit in many of these studies is the notion that potential partners are also co-located in space: they tend to live close by. Spatial homogamy, or shared similarity in geographical background, is the topic of the present study. In a recent study, new cohabiters in the Netherlands were found to choose spatially homogamous partners (Haandrikman et al. 2008a). The explorative study found considerable regional variation in spatial homogamy. This article aims to explain the regional variation in spatial homogamy by means of a spatial regression. Three sets of explanations are taken into account. First, based on the literature on marital distances, compositional factors that have been found to affect spatial homogamy—most importantly demographic and socio-economic status attributes— are considered. Second, specific spatial determinants are examined so as to account for the variation in spatial homogamy. Third, regional cultural differences, particularly variation in religion, language and value orientations, may be related to regional differences in marital distances. Recent developments in the compilation and linkage of large micro-level datasets have enabled us to conduct a large-scale study on spatial homogamy in the Netherlands. As we are interested in the spatial dimension, we aggregated micro data of all new cohabiters in 2004, taken from the population register, and linked these to geographic coordinates for each separate household address, in order to make a regional comparison. The dependent variable that was used is a so-called standardized distance coefficient, which corrects the average distance to partners to the average distance to all other Dutchmen. Subsequently, explanatory variables were derived from different sources. Besides using annual regional statistics from Statistics Netherlands and regional cultural indicators, micro-level data on educational enrolment was linked to all cohabiters, out of which regional indicators were constructed. Exploratory spatial data analysis was used to analyse the dependent and independent variables using GeoDa, and spatial regression techniques were applied to explain regional patterns of spatial homogamy.
The Spatial Dimension of Partner Choice: Background and Expectations The spatial dimension is a relatively unexplored dimension of homogamy. In the United States in the 1940s and 50s, so-called propinquity studies were conducted, in which the proximity of bride and groom before marriage was examined (e.g. Bossard 1932; Davie and Reeves 1939; Ellsworth 1948; Koller 1948). Most studies found that the number of marriages declines as the distance between potential spouses increases. For example, Bossard (1932) found that one-third of all married couples lived within five blocks from each other before marriage. Van Poppel and Ekamper (2005) provide an overview of different historical studies that prove the existence of geographical endogamy in the Netherlands. However, most studies are outdated, based on historical data, and usually restricted to cities or regions. A recent study (Haandrikman et al. 2008a) showed that Dutch people choose spatially homogamous partners: half of all new cohabiters find their partner within a 6-km distance.
Explaining Spatial Homogamy
Geographical distance influences partner choice in four ways, as described by Haandrikman et al. (2008a). Proximity increases the likelihood of spontaneous encounters, and therefore distance decay is highly pertinent in partner choice. Second, notwithstanding increases in mobility, educational enrolment and leisure time, bridging distance (still) involves time, energy and costs, and therefore partner choice still occurs at a local scale. Thirdly, physical barriers, population density and degree of urbanisation influence the access to potential partners and therefore impact meeting opportunities. Living in peripheral areas leads to average longer travel distances to partners given the accessibility to potential partners, which is further limited by spatial barriers such as water masses and mountain ranges. Fourth, the spatial pattern of potential candidates with certain characteristics influences partner choice. Geographical clustering of religion, dialect or other cultural assets, but also of socio-economic attributes may imply cultural proximity, leading to the preference of a spatially homogamous partner. The preference for a partner with the same cultural qualities stimulates the choice of a partner from the same or a culturally related region, since people in the same or related regions share the same language and are assumed to share the same ideas concerning partnerships, family, and religion (Van Poppel and Ekamper 2005). Regional variation in spatial homogamy results from different processes. The following paragraphs describe these explanatory processes and discuss the expectations for the current study. First, from the literature on marital distances, several compositional factors have been found to affect spatial homogamy, most importantly demographic and socioeconomic status attributes. Spatial clustering of people with the same characteristics, which is very common (e.g. Winch 1971; Goode 1982), may lead to patterns of regionally differentiated behaviour. As age homogamy is more common than age heterogamy (for instance Van Poppel et al. 2001; De Graaf et al. 2003), the availability of potential partners in certain age groups affects meeting and mating opportunities. The most extreme case is the so-called ‘marriage squeeze’, where men or women are confronted by a shortage of partners their age because of variations in birth numbers (Ni Bhrolcháin 2001). Another compositional effect found to affect spatial homogamy patterns is socio-economic status. Higher social classes are generally associated with longer distances between marriage partners (e.g. Küchemann et al. 1974; Coleman and Haskey 1986; Clegg et al. 1998; Van Poppel and Ekamper 2005; Haandrikman et al. 2008b). A combination of preferences, strong norms to marry within the class, and geographically extensive opportunities to meet partners might lead to greater distances. For the UK, Fielding (1992) found that higher education led to widening horizons of the middle class: these groups tend to find partners in other regions. Perhaps this is also related to the fact that education is a strong proxy for cultural lifestyle (Hendrickx 1998). Especially in the past, the lower social classes were more often locally oriented, partly due to limited (travelling) means. People of similar socio-economic status tend to cluster in space (Winch 1971), which probably leads to regional differences in marital distances between different socio-economic groups. Second, regional variation in spatial homogamy may also be explained by specific spatial determinants. In urban areas, partners may be found at shorter distances, since high concentrations of people, jobs and educational opportunities increase
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meeting opportunities. In peripheral areas on the other hand, a fewer number of potential candidates in near proximity might lead to greater mean distances. Third, regional differences in nuptiality have in the past been found to be related to cultural factors: regions with similar cultural characteristics showed similar patterns of marriage, even after controlling for the level of modernisation (Coale and Watkins 1986). Regional cultural differences in religion, language and value orientations are among the most studied and important variables in this regard. The geography of religion in the Netherlands has been surprisingly stable over the centuries. The south is predominantly Catholic, while the northern part is a mixed zone of liberal Protestants and non-denominationalists. Buffered between the two zones is a strip of towns and villages stretching from the southwest to the north known as the Bible belt (e.g. Knippenberg 2005). A large proportion of the people living in the Bible belt are orthodox Calvinists, who are characterised by rather traditional demographic behaviour as compared to the rest of the country. This group holds more traditional views on marriage and their fertility is relatively high. Dutch people tend to marry within their religious group; the level of endogamy differs per denomination (Hendrickx 1994). Especially Protestant denominations are more endogamous than the more liberal denominations. While religious endogamy of Catholics and re-reformed Protestants has declined since the 1930s, an upheaval was experienced in the 1980s (Hendrickx 1998). Religion was found to have a strong influence on marital distances for the first half of the twentieth century in the Netherlands (Polman 1951). Nowadays, religion still serves as a strong predictor of spatial demographic differences in the Netherlands (Sobotka and Adigüzel 2002). In spite of the ongoing secularisation, some more orthodox denominations still have a marked influence on demographic behaviour, through the shaping of attitudes concerning family matters. A recent study by Haandrikman et al. (2008a) revealed particularly high spatial homogamy in the Bible belt. Language is another key component of culture and therefore a major element of regional cultural differences. Linguistic differences are broad cultural borders, which may create linguistic groups in society (e.g. Van Langevelde 1999). Speaking a dialect or regional language may lead people to prefer partners from the same language group, as was found in the US (Stevens and Schoen 1988). Language then acts as a factor increasing cultural proximity. In the Netherlands, there are three officially recognised regional languages (as proclaimed by the European Charter for Regional or Minority Languages) besides standard Dutch, namely Frisian, Low Saxon, and Limburgish. Regional language speakers are geographically clustered; the dialect map of Daan and Blok (1969) that identified 28 geographically clustered dialect groups on the basis of the perception of dialect speakers is well known. Heeringa (2004) has shown that the three languages are spoken in areas with significant borders around them, as measured by dialect distances. Differences in value orientations may also lead to different patterns of spatial homogamy, as demographic behaviour has been found to be influenced by value changes (e.g. Van de Kaa 2001). With increasing urbanisation, the probability of wider social circles increases (Blau 1977). As a consequence of the ‘urban culture’, living in urban areas may nurture new value orientations and open-mindedness, leading to larger networks of friends and acquaintances and increasing opportunities to meet partners in a greater range of meeting places, distributed in a greater area,
Explaining Spatial Homogamy
thus widening the distance to partners. For the Netherlands, Brons (2006) studied dimensions of regional culture and found considerable regional variation in value orientations. His measurement of value orientations is derived from indirect measures of demographic behaviour, religious adherence, and voting behaviour, and it is based on Hofstede’s (1980, 1991) measurement of national cultures. For our study, three dimensions identified by Brons (2006) are pertinent: post-materialism, classic individualism, and Protestant conservatism (see Table 1). These dimensions of regional culture are expected to have an impact on spatial homogamy. High scores on post-materialism and classic individualism are related to modernisation, as they indicate an increased focus on self-development, little religious influence, and decreased focus on traditional households and families. With increasing modernisation, the geographical horizon of individuals has been found to increase (Beekink et al. 1998), as well as contacts between different groups in society (Hendrickx 1994). As changing geographical horizons are related to changing value orientations, Brons’s (2006) indices seem to be useful indicators for regional cultural differences that might account for part of the regional variation in spatial homogamy. The dimension Protestant conservatism represents conservative cultures, with high levels of male dominance and uncertainty avoidance. Given the resemblance to characteristics of Bible belt inhabitants, high scores on Protestant conservatism may be related to shorter distances to partners. Table 2 summarizes the hypotheses.
Table 1 Characteristics of dimensions of regional culturea Dimension of regional culture High score
Low score
Post-materialism
focus on material well-being
focus on self-development / self-expression co-operative and egalitarian
competitive and authoritarian
(very) small households
large households
many votes for progressive parties many votes for conservative parties environmentally conscious Classic individualism
individual is more important
national or collective interests more important
postponement of marriage and childbearing
relatively early marriage and childbearing
many votes for liberal parties Protestant conservatism
predominantly Protestant
predominantly Catholic
early marriage and childbearing
little early marriage and childbearing
traditional / large households and families male dominance a
Adapted from Brons (2006), p. 562
K. Haandrikman et al. Table 2 Hypotheses Compositional effects 1
A shortage of partners in the desired age group leads to increased distances between partners.
2
In areas with a higher level of socio-economic status, the distance to partners is greater.
Spatial determinants 3
The more urban the area, the shorter the distance to partners in that area.
Regional cultural differences 4
In the Bible belt area, distances between partners are shorter.
5
In areas where regional languages are spoken, distances between partners are shorter.
6
With increasing levels of modernisation, the distance between partners increases.
Data and Method In this section, the data sources used in this study are discussed, followed by a description of the dependent variable, the operationalisation of explanatory variables, and the methodology of the spatial data analysis. Data Sources Spatial homogamy is often examined by analysing distances between partners before marriage. In the current Dutch context, most couples either cohabit as a prelude to marriage, or cohabit as a substitute to marriage (Manting 1994). Therefore, the geographical similarity of partners in unions is examined for couples that start living together, irrespective of whether they are married or not. Geographic similarity is measured before cohabitation. For that reason, a geocoded micro-level database on cohabitation was constructed, based on register data. The Dutch population register, the so-called ‘Gemeentelijke Basisadministratie’ (GBA), is a decentralised automated population registration system, managed by the different municipalities. The register stores information on each registered inhabitant of the country, such as information on the person, parents, marriage, registered partnership, offspring, and address. As moving house or change in address is reported in the GBA, migration histories can be constructed. Individuals can be linked, through using personal identification numbers, to spouses, children, and parents. The municipal population registers are assessed to be of outstanding quality (Prins 2000). As we are interested in new cohabiters, those individuals who started living together with a partner in the year 2004 were selected. Since marriages and registered partnerships are recorded by the local registrar, these events are directly documented in the GBA. Unmarried cohabiters were identified by using household statistics which are annual statistics constructed by linking the personal lists of persons living at the same address. Statistics Netherlands use a set of rules to derive household positions, based on the relationships to the reference person, marital status, children if any, and an imputation model to determine the remaining group. If two people moved to the same address at the same date, they are classified as a single household. The imputation model is used to determine whether the remaining persons who live at the same address, form a single household. This logistic regression model, described
Explaining Spatial Homogamy
in Israëls and Harmsen (1999) and Harmsen and Israëls (2003), is based on findings from the Labour Force Survey about relations between background variables and the probability of forming a two-person household. To locate new cohabiters, i.e. couples who start living together at the same address, those who experienced a transition in household position, from any other position on January 1, 2004 to being a partner in a couple (with or without children) on January 1, 2005, were selected.1 The partners were matched to each other based on current address. The resulting dataset for 2004 contains 326,000 individuals (or 163,000 couples). Subsequently, the (former) addresses of cohabiters were linked to a digital file containing x- and y-coordinates for each known address in the Netherlands, as measured in the national coordinate system. This so-called ACN file (Adrescoördinaten Nederland) uniquely identifies each individual address through the 6-digit postal code and the house number. There are about 7 million addresses identified through ACN coordinates, covering 95% of all addresses. Spatial homogamy was operationalised by measuring the distance between former addresses of new cohabiters, and it was calculated by computing the Euclidian distance between the geographic coordinates of these addresses, in metres. For each municipality the average distance between partners was then calculated. Data on explanatory variables were derived from several sources. First, regional statistics were derived from Regional Core Statistics and the Regional Income Distribution 2004, both from Statistics Netherlands. Second, recent developments in the compilation and linkage of large micro-level datasets have provided us with the ability to match our dataset with another micro-level dataset. Our geo-coded micro data on cohabiters were linked to data from the so-called CRIHO files, in which all persons who studied at an institute of higher education in the Netherlands in the period 1986–2004 are included. The data include educational information for each year a person was registered at an institute of higher education, and information pertaining to degrees, majors taken, and so on. By matching the CRIHO files with the cohabiters file, we could establish for each cohabiter whether that person ever studied at an institute of higher education. Finally, regional cultural differences were measured by the earlier-mentioned dimensions of core value orientations proposed by Brons (2006). Dependent Variable: the Standardized Distance Coefficient In a previous study, considerable regional variation in spatial homogamy was found in the Netherlands (Haandrikman et al. 2008a). Moreover, partners living in low density areas and in the periphery were found to have lived further apart. One important factor for this result is that the average distance to any other person in the Netherlands is also greater than in the core and densely populated regions. Therefore, the distance between partners should be standardised for the average distance to all other inhabitants in the Netherlands. This is done as follows. Firstly, for a person living in municipality i we calculate the distance to all other persons in the Netherlands. For practical purposes this is approximated by aggregating to the 1
Since the imputation model may lead to overestimation of the number of cohabiting same-sex couples (Steenhof and Harmsen 2003), only heterosexual couples were selected for analysis.
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municipality level. Let dij be the distance between the geometric centres of municipality i and j. Then the average distance for any person living in i to another person in the Netherlands is approximated by: 1 X di ¼ dij Pj ð1Þ N j where Pj is the population size of municipality j and N is the population of the Netherlands. As distances to partners within the same municipality are not zero, these distances are approximated by: rffiffiffiffiffiffiffiffiffiffiffi areai 2 dii ¼ =3 ð2Þ p where areai is the area of municipality i in square metres on January 1, 2004.2 The underlying assumption of this formula is that the population is uniformly distributed within the municipality in the form of a circle. Next, let si be the average distance to cohabitation partners for all those who started cohabiting in 2004 and who were living in municipality i on January 1, 2004. The standardized distance coefficient for municipality i is calculated as: SDCi ¼
si di
ð3Þ
and is the dependent variable in the analysis. The coefficient of an area can be interpreted as anywhere in the range from a very short distance to a very long distance to partners, given the location of the area within the country. A municipality with a high coefficient might be situated in the Utrecht area (thus centrally located), with partners found at relatively long distances, whereas a low coefficient might be found in an area in the upper north, with relatively short distances to partners. A value of the coefficient of 0.5 implies that the average distance to partners in a specific area is half that of the average distance to all other Dutch people. Operationalisation of Variables The explanatory variables are listed in Table 3 and they are clarified in the subsequent paragraphs. The spatial units of analysis are the 483 municipalities of the Netherlands in the year 2004.
2
A justification for the use of 2/3 radius for the intra-zonal distance estimate: We assume a circular shape of the municipality, and a population density function FðrÞ ¼ d0 ra , where r is the distance from the centre, d0 equal to the density in the centre, and a the density decay with increasing distance to the centre. We approximate the average distance to another person in the municipality by the average R R distance to the centre. The total distance that the population covers to the centre Ris equal to: D ¼ 0 2pr2 FðrÞdr, and the total population in the municipality is equal to: R P ¼ 0 2pFðrÞdr. The average 2adistance, D/P (total distance/population) is equal to: 2a 2a 0R ¼ D=P ¼ D ¼ 2pd 1a 3a 3a R, with R being the radius of the municipality. If we assume a 2pd0 R homogeneous distribution of the population, a=0, and the average distance reduces to: D ¼ 2=3R. Finally, we have to make an estimate qffiffiffi qffiffiffi of R, based on the size of the municipality A. In a circular municipality A ¼ pR2, and thus R ¼ Ap . Therefore, the intra-zonal distance is estimated as dii ¼ 2=3 Api .
Explaining Spatial Homogamy Table 3 Explanatory variables Explanatory factors
Variables
Source
Statistics Netherlandsa
Compositional effects Demographic characteristics
Age group
Percentage of population aged 25–45 years
Socio-economic characteristics
Educational level
Percentage of cohabiters that studied CRIHO at an institute of higher education
Income
Percentage of population in lowest income group (€ 24,300)
Statistics Netherlands b
Average surrounding address density (/1,000)
Statistics Netherlands
Religion
Index for Protestant conservatism
Brons (2006)
Language
Living in Frisian-speaking area
Spatial determinants Degree of urbanisation Regional cultural differences
Living in Low Saxon-speaking area Living in Limburgish-speaking area Value orientations
a
Index for post-materialism
Brons (2006)
Index for classic individualism
Brons (2006)
From Regional Core Statistics, Statistics Netherlands
b
From Regional Income Distribution 2004, Statistics Netherlands. The data are based on registers from the Ministry of Finance and the population register (GBA), combined with a sample of 1.9 million households
Compositional effects are measured by demographic and socio-economic variables. Firstly, the percentage of the population that is aged between 25 and 45 years on January 1, 2004 was determined for each municipality. Among the new cohabiters, 65 percent of them fall within this age group (Haandrikman et al. 2008a), making this the most appropriate target group for persons looking for a partner. Socio-economic status was operationalised by educational level and income. The average educational level of cohabiters in each municipality was constructed from the CRIHO file. For each cohabiter, it was determined whether that person was recorded in the CRIHO file, thereby offering an approximation of the educational level ‘higher educated’. Then, the percentage of cohabiters that ever studied was calculated for each municipality. Income was operationalised as the total financial income from all jobs and other resources, such as real estate revenues and other assets. The income data is based on persons with 52 weeks of income, including the self-employed. Income units were distributed across ten percent classes, which are of equal size. The data include persons living in one of the 467 municipalities on January 1, 2005. Because of municipal redistributions since 2004, several adaptations were made.3 For 3
For 20 municipalities that ceased to exist as per January 1, 2005, mostly in the province of Gelderland, income data from the Regional Income Distribution 2003 is used instead. Moreover, two municipalities have missing data on the lowest income percentile, namely Rozendaal and Thorn. For these municipalities, the average of the adjacent municipalities is taken instead.
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the regional analysis, the percentage of inhabitants in the lowest and the highest income group in each municipality was included. Spatial determinants of regional variation in spatial homogamy were operationalised by examining the degree of urbanisation of the municipalities. Statistics Netherlands annually measures the extent of concentration of human activities (houses, jobs, schools, shops, pubs, and so forth) by calculating the average surrounding address density. The surrounding address density is the number of addresses within a circular area around an address with a radius of 1 km, divided by the square of the circle, and it is calculated for each 500 by 500 m2 containing at least one address. The resulting variable is expressed in the number of addresses per square kilometre. For the regional analysis, the average surrounding address density per municipality, calculated for each 500 by 500 m2 per January 1, 2004, was used, divided by 1,000.4 Regional cultural differences were operationalised through a set of value orientations and language variables. As religion is not documented on a large scale, it was operationalised through Brons’s value orientation ‘Protestant conservatism’, as it is most strongly related to religion, especially Protestantism (Brons 2006). Language was operationalised by distinguishing three core areas in which Frisian, Low Saxon, and Limburgish are spoken. Frisian is widely spoken in the province of Friesland, whereas Limburgish is the regional language of Limburg. Low Saxon is spoken in a larger area, namely in Groningen, Drenthe, Overijssel and parts of Gelderland, which were classified as Low Saxon-speaking areas. Local value orientations were operationalised by two dimensions of core value orientations, namely post-materialism and classic individualism, as they approximate measures of modernisation.5 These regional variables were measured at the municipal level, and were based on demographic, religious and voting behaviour in the period from 1997 to 2003. As we expect these value orientations not to have changed within 1 year, the data has been applied to the municipalities of the year 2004.6 They were matched to the municipality where cohabiters lived before they started living together with their partner on January 1, 2004. Methodology of the Spatial Data Analysis Exploratory spatial data analysis was used to analyse the dependent and independent variables, using ArcGIS and GeoDa (Anselin et al. 2006). Spatial regression techniques were applied to explain spatial patterns of spatial homogamy. In spatial analyses, spatial autocorrelation may cause problems. In our study, there is a mismatch between the spatial unit of analysis, i.e. municipalities, and the spatial extent of local partner markets. We know that in 2004, the average distance to a cohabitation 4
The Pearson correlation between the degree of urbanisation and the average distance to all other inhabitants (the numerator of the standardized distance coefficient) is −0.26 (p
